A decentralised online multi-agent planning framework for multi-agent systems

• Ano de defesa: 2018
• Autor(a) principal: Cardoso, Rafael Cauê
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Escola Politécnica; Brasil; PUCRS; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Multi-agent Planning; Multi-agent Systems; Hierarchical Task Network; Goal Allocation; Online Planning; Planejamento Multiagente; Sistemas Multiagentes; Rede de Tarefas Hierárquicas; Alocação de Objetivos; Planejamento Online; CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8048

Multi-agent systems often contain dynamic and complex environments where agents’ course of action (plans) can fail at any moment during execution of the system. Furthermore, new goals can emerge for which there are no known plan available in any of the agents’ plan library. Automated planning techniques are well suited to tackle both of these issues. Extensive research has been done in centralised planning for singleagents, however, so far multi-agent planning has not been fully explored in practice. Multi-agent platforms typically provide various mechanisms for runtime coordination, which are often required in online planning (i.e., planning during runtime). In this context, decentralised multi-agent planning can be efficient as well as effective, especially in loosely-coupled domains, besides also ensuring important properties in agent systems such as privacy and autonomy. We address this issue by putting forward an approach to online multi-agent planning that combines goal allocation, individual Hierarchical Task Network (HTN) planning, and coordination during runtime in order to support the achievement of social goals in multi-agent systems. In particular, we present a planning and execution framework called Decentralised Online Multi-Agent Planning (DOMAP). Experiments with three loosely-coupled planning domains show that DOMAP outperforms four other state-of-the-art multi agent planners with regards to both planning and execution time, particularly in the most difficult problems.